Abstract: Cassava is the third largest source of carbohydrates for human food in the
world but is vulnerable to virus diseases, which threaten to destabilize food
security in sub-Saharan Africa. Novel methods of cassava disease detection are
needed to support improved control which will prevent this crisis. Image
recognition offers both a cost effective and scalable technology for disease
detection. New transfer learning methods offer an avenue for this technology to
be easily deployed on mobile devices. Using a dataset of cassava disease images
taken in the field in Tanzania, we applied transfer learning to train a deep
convolutional neural network to identify three diseases and two types of pest
damage (or lack thereof). The best trained model accuracies were 98% for brown
leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage
(GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic
disease (CMD). The best model achieved an overall accuracy of 93% for data not
used in the training process. Our results show that the transfer learning
approach for image recognition of field images offers a fast, affordable, and
easily deployable strategy for digital plant disease detection.